Cyber Threat Information Sharing Platforms are carefully engineered systems in which users of the systems contribute to the reporting of cyber threat information. However, with wider adoption of these platforms research is required to improve the utility to be gained from the reported cyber threat information.
The project aims to consider the application of machine learning and decision support strategies to make sense of vast array of cyber threat information. It will define models for identifying attack vectors, levels of trust in users, and then identifying changes of user behaviour that indicate propensity to move from trusted to threatening behaviour.
This research is supported by the UK Government and it is in partnership with Surevine and BAE Systems Applied Intelligence. The PhD student will work at the University of Surrey, at the SCCS (Surrey Centre for Cyber Security) and with CVSSP (Centre for Vision and Signal Processing).
If you are fascinated about machine learning and security and you have knowledge of any of the following: machine learning, cyber risks, web development and APIs, please contact the supervisor for any further enquiries about the post or apply directly!
The project will commence in October 2019 and lasts 3.5 years.
• Bachelor degree in Computer Science (UK equivalent 1st classification)
• Interest in the following: cyber security, machine learning
• Analytical skills: knowledge of foundations of computer science; ability to think independently
• Strong verbal and written communication skills, both in plain English and scientific language for publication in relevant journals and presentation at conferences.
• Master’s degree (UK equivalent of Merit classification or above)
• Knowledge of cyber security and computer networks
• Experience in machine learning
• An understanding of different authentication mechanisms including OAuth 2.0
How to apply:
Please apply through our course page and include the studentship title in your application: https://www.surrey.ac.uk/postgraduate/computer-science-phd